本文選題：智能移動(dòng)終端 + 室內(nèi)定位��； 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：近年來,基于室內(nèi)位置的服務(wù)需求變得越來越強(qiáng)烈。基于聲信號(hào)的室內(nèi)定位技術(shù),由于具有兼容性好、穩(wěn)定性和定位精度高,使其成為近幾年的前沿研究課題。當(dāng)前大多數(shù)的智能移動(dòng)終端均具有揚(yáng)聲器和麥克風(fēng),這使得此類系統(tǒng)極易在實(shí)際環(huán)境中得到應(yīng)用與推廣。但實(shí)際上在真實(shí)的室內(nèi)環(huán)境中,存在人員走動(dòng)、家具遮擋、墻壁反射等等復(fù)雜的因素,使得聲源與接收器之間的直接路徑被遮擋,稱為非視距(Non-Line-of-Sight,NLOS)。由于距離的量測(cè)依賴于聲信號(hào)的時(shí)延估計(jì),NLOS傳播往往會(huì)給距離的估計(jì)帶來較大誤差,使得傳統(tǒng)基于視距(Line-of-Sight,LOS)環(huán)境下提出的定位算法失效,導(dǎo)致定位精度急速下降。本文針對(duì)室內(nèi)NLOS/LOS復(fù)雜環(huán)境,提出了一種基于NLOS判別的非視距定位方法,以及基于聲信號(hào)信道統(tǒng)計(jì)特征NLOS識(shí)別的定位算法。本文的主要工作內(nèi)容和貢獻(xiàn)包含以下幾個(gè)方面:首先,基于室內(nèi)聲音傳播模型,對(duì)LOS環(huán)境和NLOS環(huán)境下聲音信號(hào)信道特性進(jìn)行研究。針對(duì)室內(nèi)復(fù)雜環(huán)境中的強(qiáng)多徑傳輸以及多普勒頻移等因素,提出了基于互相關(guān)的聲信道相對(duì)參數(shù)估計(jì)方法(相對(duì)時(shí)延及相對(duì)增益),降低了多普勒效應(yīng)的影響,降低了計(jì)算復(fù)雜度。并提出一種基于信噪比(SNR)的自適應(yīng)閾值函數(shù),對(duì)聲信號(hào)第一徑到達(dá)時(shí)刻進(jìn)行判別,降低了多徑傳輸?shù)挠绊�。其�?基于聲信道參數(shù)估計(jì),對(duì)信道特性的特征提取進(jìn)行研究,包括時(shí)延特性、波形形狀特性、RicianK系數(shù),以及增益的幅值分布特性,共9個(gè)用于NLOS識(shí)別的特征值,利用基于支持向量機(jī)的分類器,實(shí)現(xiàn)對(duì)NLOS信號(hào)的識(shí)別分類,并對(duì)其核函數(shù)及特征值組合進(jìn)行了最優(yōu)選取。最后,在NLOS信號(hào)識(shí)別的基礎(chǔ)上,針對(duì)靜態(tài)目標(biāo)的NLOS定位,提出了基于NLOS識(shí)別剔除的定位策略和基于NLOS后驗(yàn)概率的加權(quán)最小二乘定位策略。針對(duì)移動(dòng)目標(biāo)提出了基于NLOS識(shí)別的修正卡爾曼濾波追蹤算法和基于NLOS后驗(yàn)概率修正卡爾曼濾波追蹤算法,以實(shí)現(xiàn)室內(nèi)NLOS/LOS混合環(huán)境下的魯棒定位追蹤。
[Abstract]:In recent years, the service demand based on indoor location has become more and more intense. Because of its good compatibility, high stability and high positioning accuracy, acoustic signal based indoor positioning technology has become a frontier research topic in recent years. At present, most intelligent mobile terminals have loudspeakers and microphones, which makes such systems easy to be applied and popularized in real environment. But in the real indoor environment, there are complicated factors such as walking of personnel, furniture occlusion, wall reflection and so on, which make the direct path between the sound source and the receiver blocked, which is called Non-Line-of-SightNLOS (Non-Line-of-SightNLOS). Because the measurement of distance depends on the time delay estimation of acoustic signal, NLOS propagation often brings great error to the estimation of distance, which makes the traditional localization algorithm based on Line-of-SightLos environment invalid, resulting in the rapid decline of location accuracy. In this paper, a non-line-of-sight location method based on NLOS discrimination and a localization algorithm based on the statistical characteristics of acoustic signal channel NLOS are proposed for the indoor NLOSP-Los complex environment. The main contents and contributions of this paper are as follows: firstly, based on the indoor sound propagation model, the channel characteristics of acoustic signals in Los and NLOS environments are studied. In view of the factors such as strong multipath transmission and Doppler frequency shift in complex indoor environment, a cross-correlation-based method for estimating the relative parameters of acoustic channels (relative delay and relative gain) is proposed, which reduces the influence of Doppler effect. The computational complexity is reduced. An adaptive threshold function based on signal-to-noise ratio (SNR) is proposed to distinguish the first arrival time of acoustic signal and reduce the influence of multipath transmission. Secondly, based on the estimation of acoustic channel parameters, the characteristic extraction of channel characteristics is studied, including delay characteristic, waveform shape characteristic and RicianK coefficient, as well as amplitude distribution characteristics of gain. Nine characteristic values are used for NLOS recognition. A classifier based on support vector machine (SVM) is used to realize the recognition and classification of NLOS signals, and the combination of kernel function and eigenvalue is selected optimally. Finally, on the basis of NLOS signal recognition, the location strategy based on NLOS recognition and elimination and the weighted least square location strategy based on NLOS posteriori probability are proposed for NLOS localization of static targets. A modified Kalman filter tracking algorithm based on NLOS recognition and a modified Kalman filter tracking algorithm based on NLOS posteriori probability are proposed to achieve robust location tracking in NLOS- Los hybrid environment.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN912.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 劉征宇;;精準(zhǔn)營(yíng)銷方法研究[J];上海交通大學(xué)學(xué)報(bào);2007年S1期

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本文編號(hào)：2087696